scholarly journals On wake modeling, wind-farm gradients, and AEP predictions at the Anholt wind farm

2018 ◽  
Vol 3 (1) ◽  
pp. 191-202 ◽  
Author(s):  
Alfredo Peña ◽  
Kurt Schaldemose Hansen ◽  
Søren Ott ◽  
Maarten Paul van der Laan
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Wind Farm ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Westerly Flow ◽  
Wind Climate ◽  
Mesoscale Simulations ◽  
Inflow Conditions ◽  
Speed Gradient

Abstract. We investigate wake effects at the Anholt offshore wind farm in Denmark, which is a farm experiencing strong horizontal wind-speed gradients because of its size and proximity to land. Mesoscale model simulations are used to study the horizontal wind-speed gradients over the wind farm. From analysis of the mesoscale simulations and supervisory control and data acquisition (SCADA), we show that for westerly flow in particular, there is a clear horizontal wind-speed gradient over the wind farm. We also use the mesoscale simulations to derive the undisturbed inflow conditions that are coupled with three commonly used wake models: two engineering approaches (the Park and G. C. Larsen models) and a linearized Reynolds-averaged Navier–Stokes approach (Fuga). The effect of the horizontal wind-speed gradient on annual energy production estimates is not found to be critical compared to estimates from both the average undisturbed wind climate of all turbines' positions and the undisturbed wind climate of a position in the middle of the wind farm. However, annual energy production estimates can largely differ when using wind climates at positions that are strongly influenced by the horizontal wind-speed gradient. When looking at westerly flow wake cases, where the impact of the horizontal wind-speed gradient on the power of the undisturbed turbines is largest, the wake models agree with the SCADA fairly well; when looking at a southerly flow case, where the wake losses are highest, the wake models tend to underestimate the wake loss. With the mesoscale-wake model setup, we are also able to estimate the capacity factor of the wind farm rather well when compared to that derived from the SCADA. Finally, we estimate the uncertainty of the wake models by bootstrapping the SCADA. The models tend to underestimate the wake losses (the median relative model error is 8.75 %) and the engineering wake models are as uncertain as Fuga. These results are specific for this wind farm, the available dataset, and the derived inflow conditions.

scholarly journals On wake modeling, wind-farm gradients and AEP predictions at the Anholt wind farm

2017 ◽  
Author(s):  
Alfredo Peña ◽  
Kurt Schaldemose Hansen ◽  
Søren Ott ◽  
Maarten Paul van der Laan
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Wind Farm ◽  
Mesoscale Model ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Westerly Flow ◽  
Wake Model ◽  
Inflow Conditions ◽  
Speed Gradient

Abstract. We investigate wake effects at the Anholt offshore wind farm in Denmark. We perform the analysis with three commonly-used wake models; two engineering approaches (the Park and G. C. Larsen models) and a linearized Reynolds-averaged Navier-Stokes approach (Fuga). From analysis of SCADA and mesoscale model simulations, we show that for westerly flow in particular, there is a clear horizontal wind-speed gradient over the wind farm, which results from the effect of the land nearby. We also show that for annual energy production estimates, in which a wake model is run with inflow conditions derived from mesoscale model outputs, accounting for the horizontal wind-speed gradient gives nearly the same results as averaging all the wake-free wind climates at the turbines' positions or using the wind climate of a position in the middle of the wind farm. However, annual energy production estimates can largely differ when using wind climates that are strongly influenced by the wind-speed gradient. When looking at westerly flow wake cases, where the impact of the wind-speed gradient is largest, the wake models agree with the SCADA fairly well; when looking at a southerly flow case, where the wake losses are highest, they tend to underestimate the wake loss. With the mesoscale-wake model setup, we are also able to estimate the capacity factor of the wind farm rather well when compared to that derived from the SCADA. Finally, we estimate the uncertainty of the wake models and some of its variants by bootstrapping the SCADA. The models tend to underestimate the wake losses and the engineering wake models are as uncertain as Fuga. These results are specific for this wind farm, the available dataset, and the derived inflow conditions.

scholarly journals Field Measurements of Wind Characteristics Using LiDAR on a Wind Farm with Downwind Turbines Installed in a Complex Terrain Region

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5135
Author(s):  
Tetsuya Kogaki ◽  
Kenichi Sakurai ◽  
Susumu Shimada ◽  
Hirokazu Kawabata ◽  
Yusuke Otake ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Field Experiments ◽  
Wind Farms ◽  
Field Measurements ◽  
Horizontal Wind ◽  
Flow Distortion ◽  
Horizontal Wind Speed ◽  
Vertical Profiling ◽  
Wind Characteristics

Downwind turbines have favorable characteristics such as effective energy capture in up-flow wind conditions over complex terrains. They also have reduced risk of severe accidents in the event of disruptions to electrical networks during strong storms due to the free-yaw effect of downwind turbines. These favorable characteristics have been confirmed by wind-towing tank experiments and computational fluid dynamics (CFD) simulations. However, these advantages have not been fully demonstrated in field experiments on actual wind farms. In this study—although the final objective was to demonstrate the potential advantages of downwind turbines through field experiments—field measurements were performed using a vertical-profiling light detection and ranging (LiDAR) system on a wind farm with downwind turbines installed in complex terrains. To deduce the horizontal wind speed, vertical-profiling LiDARs assume that the flow of air is uniform in space and time. However, in complex terrains and/or in wind farms where terrain and/or wind turbines cause flow distortion or disturbances in time and space, this assumption is not valid, resulting in erroneous wind speed estimates. The magnitude of this error was evaluated by comparing LiDAR measurements with those obtained using a cup anemometer mounted on a meteorological mast and detailed analysis of line-of-sight wind speeds. A factor that expresses the nonuniformity of wind speed in the horizontal measurement plane of vertical-profiling LiDAR is proposed to estimate the errors in wind speed. The possibility of measuring and evaluating various wind characteristics such as flow inclination angles, turbulence intensities, wind shear and wind veer, which are important for wind turbine design and for wind farm operation is demonstrated. However, additional evidence of actual field measurements on wind farms in areas with complex terrains is required in order to obtain more universal and objective evaluations.

scholarly journals Documenting Wind Speed and Power Deficits behind a Utility-Scale Wind Turbine

2013 ◽  
Vol 52 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Brian D. Hirth ◽  
John L. Schroeder
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Large Variability ◽  
Cost Of Energy ◽  
The Mean ◽  
Utility Scale ◽  
Turbine Wake

AbstractHigh-spatial-and-temporal-resolution radial velocity measurements surrounding a single utility-scale wind turbine were collected using the Texas Tech University Ka-band mobile research radars. The measurements were synthesized to construct the first known dual-Doppler analyses of the mean structure and variability of a single turbine wake. The observations revealed a wake length that subjectively exceeded 20 rotor diameters, which far exceeds the typically employed turbine spacing of 7–10 rotor diameters. The mean horizontal wind speed deficits found within the turbine wake region relative to the free streamflow were related to potential reductions in the available power for a downwind turbine. Mean wind speed reductions of 17.4% (14.8%) were found at 7 (10) rotor diameters downwind, corresponding to a potential power output reduction of 43.6% (38.2%). The wind speed deficits found within the wake also exhibit large variability over short time intervals; this variability would have an appreciable impact on the inflow of a downstream turbine. The full understanding and application of these newly collected data have the potential to alter current wind-farm design and layout practices and to affect the cost of energy.

scholarly journals On the Relationships between the Madden–Julian Oscillation and Precipitation Variability in Southern Iran and the Arabian Peninsula: Atmospheric Circulation Analysis

2010 ◽  
Vol 23 (4) ◽  
pp. 887-904 ◽  
Author(s):  
M. J. Nazemosadat ◽  
H. Ghaedamini
Keyword(s):  
Wind Speed ◽  
Arabian Peninsula ◽  
The Other ◽  
Negative Phase ◽  
Horizontal Wind ◽  
Seasonal Precipitation ◽  
Madden Julian Oscillation ◽  
Horizontal Wind Speed ◽  
Southern Iran ◽  
Speed Gradient

Abstract The influence of the Madden–Julian oscillation (MJO) on daily, monthly, and seasonal precipitation was investigated for southern Iran and the Arabian Peninsula using November–April data for the period of 1979–2005. The positive MJO phase is considered to be the periods for which the enhanced convection center was placed over the south Indonesian–north Australian region. On the other hand, the convection center shifts over the western Indian Ocean tropics and most of the study area as the negative MJO phase prevails. Seasonal precipitation and the frequency of wet events were significantly increased during the negative phase. The ratios of the precipitation amount during the negative phase to the corresponding values during the positive phase were about 1.75–2.75 and 2.75–4.00 for the southwestern and southeastern parts of Iran, respectively. This ratio reached to about 3.00 for Riyadh, 4.20 and 5.50 for Masqat and Doha, 2.10 for Kuwait, and 1.20 for Bahrain. The results of the seasonal and monthly analysis were generally found to be consistent, although because of the smaller sample size the outcomes of the monthly investigations were less statistically significant. While the negative MJO phase does not have a consistent effect on March precipitation over some parts of southern Iran, it has consistently enhanced precipitation over the eastern and southern coasts of the peninsula in Oman, Yemen, and Saudi Arabia. During the negative MJO phase, while enhanced low-level southerly winds transfer a substantial amount of moisture to the study area, upward motion increases in the middle layers of the atmosphere. Synchronized with the prevalence of these rain-bearing southerly winds, the existence of a strong horizontal wind speed gradient at the exit region of the North Africa–Arabian jet enhances precipitation. The jet exit, which was mostly located over Egypt in November, moved westward into the study area in Iran and Saudi Arabia during the rainy period of January–March. The direction of near-surface wind anomalies changed from mostly southeasterly in November to southwesterly in March and April, influencing precipitation pattern during various months of the rainy season. In contrast to the negative phase, an enhanced low-level dry northerly wind and suppressed horizontal wind speed gradient at the jet exits are the main characteristics of atmospheric circulation over the study area during the positive MJO phase. Furthermore, an increased downward air motion at the middle levels of the atmosphere and a significant shortage in precipitation are the other climatic components of the southwest Asian region during such a period.

scholarly journals Doppler wind lidar activities at Cabauw

2021 ◽  
Author(s):  
Steven Knoop ◽  
Fred Bosveld ◽  
Marijn de Haij ◽  
Arnoud Apituley
Keyword(s):  
Wind Speed ◽  
Wind Profile ◽  
Continuous Wave ◽  
Wind Farms ◽  
Data Availability ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Low Clouds ◽  
Wind Measurements ◽  
Wind Lidar

<p>Atmospheric motion and turbulence are essential parameters for weather and topics related to air quality. Therefore, wind profile measurements play an important role in atmospheric research and meteorology. One source of wind profile data are Doppler wind lidars, which are laser-based remote sensing instruments that measure wind speed and wind direction up to a few hundred meters or even a few kilometers. Commercial wind lidars use the laser wavelength of 1.5 µm and therefore backscatter is mainly from aerosols while clear air backscatter is minimal, limiting the range to the boundary layer typically.</p><p>We have carried out a two-year intercomparison of the ZephIR 300M (ZX Lidars) short-range wind lidar and tall mast wind measurements at Cabauw [1]. We have focused on the (height-dependent) data availability of the wind lidar under various meteorological conditions and the data quality through a comparison with in situ wind measurements at several levels in the 213m tall meteorological mast. We have found an overall availability of quality-controlled wind lidar data of 97% to 98 %, where the missing part is mainly due to precipitation events exceeding 1 mm/h or fog or low clouds below 100 m. The mean bias in the horizontal wind speed is within 0.1 m/s with a high correlation between the mast and wind lidar measurements, although under some specific conditions (very high wind speed, fog or low clouds) larger deviations are observed. This instrument is being deployed within North Sea wind farms.</p><p>Recently, a scanning long-range wind lidar Windcube 200S (Leosphere/Vaisala) has been installed at Cabauw, as part of the Ruisdael Observatory program [2]. The scanning Doppler wind lidars will provide detailed measurements of the wind field, aerosols and clouds around the Cabauw site, in coordination with other instruments, such as the cloud radar.</p><p>[1] Knoop, S., Bosveld, F. C., de Haij, M. J., and Apituley, A.: A 2-year intercomparison of continuous-wave focusing wind lidar and tall mast wind measurements at Cabauw, Atmos. Meas. Tech., 14, 2219–2235, 2021</p><p>[2] https://ruisdael-observatory.nl/</p>

scholarly journals Comparison of Large Eddy Simulations of a convective boundary layer with wind LIDAR measurements

2012 ◽  
Vol 8 (1) ◽  
pp. 83-86 ◽  
Author(s):  
J. G. Pedersen ◽  
M. Kelly ◽  
S.-E. Gryning ◽  
R. Floors ◽  
E. Batchvarova ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Geostrophic Wind ◽  
Large Eddy Simulations ◽  
Horizontal Wind ◽  
Convective Boundary ◽  
Horizontal Wind Speed ◽  
Large Eddy ◽  
Lidar Measurements ◽  
Wind Lidar

Abstract. Vertical profiles of the horizontal wind speed and of the standard deviation of vertical wind speed from Large Eddy Simulations of a convective atmospheric boundary layer are compared to wind LIDAR measurements up to 1400 m. Fair agreement regarding both types of profiles is observed only when the simulated flow is driven by a both time- and height-dependent geostrophic wind and a time-dependent surface heat flux. This underlines the importance of mesoscale effects when the flow above the atmospheric surface layer is simulated with a computational fluid dynamics model.

Wind resource assessment and wind farm modeling in Mossobo-Harena area, North Ethiopia

2020 ◽  
pp. 0309524X2092540
Author(s):  
Addisu Dagne Zegeye
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Power Density ◽  
Energy Production ◽  
Wind Farm ◽  
Ground Level ◽  
Energy Potential ◽  
Mean Power ◽  
Wind Energy Potential

Although Ethiopia does not have significant fossil fuel resource, it is endowed with a huge amount of renewable energy resources such as hydro, wind, geothermal, and solar power. However, only a small portion of these resources has been utilized so far and less than 30% of the nation’s population has access to electricity. The wind energy potential of the country is estimated to be up to 10 GW. Yet less than 5% of this potential is developed so far. One of the reasons for this low utilization of wind energy in Ethiopia is the absence of a reliable and accurate wind atlas and resource maps. Development of reliable and accurate wind atlas and resource maps helps to identify candidate sites for wind energy applications and facilitates the planning and implementation of wind energy projects. The main purpose of this research is to assess the wind energy potential and model wind farm in the Mossobo-Harena site of North Ethiopia. In this research, wind data collected for 2 years from Mossobo-Harena site meteorological station were analyzed using different statistical software to evaluate the wind energy potential of the area. Average wind speed and power density, distribution of the wind, prevailing direction, turbulence intensity, and wind shear profile of the site were determined. Wind Atlas Analysis and Application Program was used to generate the generalized wind climate of the area and develop resource maps. Wind farm layout and preliminary turbine micro-sitting were done by taking various factors into consideration. The IEC wind turbine class of the site was determined and an appropriate wind turbine for the study area wind climate was selected and the net annual energy production and capacity factor of the wind farm were determined. The measured data analysis conducted indicates that the mean wind speed at 10 and 40 m above the ground level is 5.12 and 6.41 m/s, respectively, at measuring site. The measuring site’s mean power density was determined to be 138.55 and 276.52 W/m2 at 10 and 40 m above the ground level, respectively. The prevailing wind direction in the site is from east to south east where about 60% of the wind was recorded. The resource grid maps developed by Wind Atlas Analysis and Application Program on a 10 km × 10 km area at 50 m above the ground level indicate that the selected study area has a mean wind speed of 5.58 m/s and a mean power density of 146 W/m2. The average turbulence intensity of the site was found to be 0.136 at 40 m which indicates that the site has a moderate turbulence level. According to the resource assessment done, the area is classified as a wind Class IIIB site. A 2-MW rated power ENERCON E-82 E2 wind turbine which is an IEC Class IIB turbine with 82 m rotor diameter and 98 m hub height was selected for estimation of annual energy production on the proposed wind farm. 88 ENERCON E-82 E2 wind turbines were properly sited in the wind farm with recommended spacing between the turbines so as to reduce the wake loss. The rated power of the wind farm is 180.4 MW and the net annual energy production and capacity factor of the proposed wind farm were determined to be 434.315 GWh and 27.48% after considering various losses in the wind farm.

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